Planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum

Planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum

Li, Y. (China University of Mining and Technology)
Cheng, G. (China University of Mining and Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)
Kuai, Moshen (China University of Mining and Technology)

2018

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.

Center frequency
CNN
Fault diagnosis
Feature image
Planetary gear
VMD

http://resolver.tudelft.nl/uuid:4265fa23-53c3-4663-b207-cf33828844cf

https://doi.org/10.3390/s18061735

1424-8220

Sensors, 18 (6)

Institutional Repository

journal article

© 2018 Y. Li, G. Cheng, Y. Pang, Moshen Kuai